Forecasting is becoming increasingly important in corporate sustainability governance, as is government governance, and the prediction of police crime hotspots is related to human rights, so transparency is needed. There are many ways to predict hotspots of criminal activity in urban areas. Experts assume that if many crimes occur somewhere, even more, are likely to happen at subsequent times. Such predictions may rely on a state dependency model such as the Poisson distribution algorithm to formulate re-occurrence, its results can provide a visualized hotspot map with Q-GIS maps. Forecasting sets the threshold for re-occurrence and affects the distribution of the forecast. This paper studies the occurrence of criminal activity in urban areas, refers to the metrics set by the NIJ’s crime prediction contest and focuses on the presentation of the results by accumulating different historical data. It was determined that when the amount of cumulative data is greater, its prediction measures by the prediction accuracy index (PAI) insures that accuracy is improved, but the prediction efficiency index (PEI) that efficiency level is worse. Because threshold setting directly affects the performance of the forecast, it can be used differently. Here sets four different indicators, hit rate, useful rate, waste rate, and missing rate. It was determined that the hit rate, missing rate, the PAI value, and the PEI value are directly proportional to the threshold value, while the trend of useful rate and waste rate are inversely related. Concerned policymakers can set different thresholds dependent up the number and budgetary constraints of police forces, and they can work towards achieving crime prevention in urban hotspots. Importantly, Poisson’s approach can be simply implemented with Excel, be conducive to drive by the office practitioner, and elevate the transparency of crime prediction.

Compared to government workloads which are often irregular, police work contains many aspects, each of which is held to specific performance standards. Past literature has investigated individual cases and made selective comparisons, yet is lacking in overall work analyses. Police authorities must be able to assess the work performances of each police station to ensure the balance of investments and returns while also investigating overall work performance, rather than solely looking at individual cases. This study used objective analysis to evaluate the 2014 work performances of Kaohsiung City Police Department police stations (Taiwan) in 00 Precinct in order to determine the quality of work performance so that the heads of each unit can improve any insufficiencies in accordance with objectives and improve the competitiveness of police work. As such, the Delphi Technique was used to calculate the weight for several key indicators for 00 Precinct; then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to arrange these indicators according to 00 Precinct police station work performance ratings. Nine police stations (1 A-9 A) and seven indicators (number of police officers, population in area of jurisdiction, total number of criminal cases, number of solved cases, number of traffic accidents, number of traffic tickets issued, and number of cases accepted via 110) were included in this study to assess the work performances of each station. The number of police officers was divided by the population in area of jurisdiction to obtain a police-to-population ratio for a total of six C values. The results indicated that among the nine police stations, 1 A had the highest administrative performance in 00 Precinct; its i C value of 55.95% showed that this police station had the best work performance.